1. Field
This disclosure relates to power control for a network of power stations. In a particular configuration, inverter power settings are performed for multiple solar panel stations.
2. Background
Solar photovoltaic systems produce electrical power. Electrical power is the product of current and voltage (I×V). Operating point and output power are interdependent in individual solar cells, and by extension in multi-cell panels and multi-panel arrays. The interdependence is characterized by a set of “I-V curves” as shown in FIG. 1. Each I-V curve has a “Maximum Power Point” (MPP). This point is the operating point (voltage and current) at which the product of the panel's voltage and current provides the highest possible power output for a given set of environmental conditions (the peaks of the curves on the lower graph of FIG. 1). In viewing FIG. 1, MPPhigh is the point on the voltage axis at which the power is maximum for the upper curve; MPPmedium is the point on the voltage axis at which the power is maximum for the middle curve; and MPPlow, is the point on the voltage axis at which the power is maximum for the lower curve. These are illustrative graphs, but a typical value for the MPPhigh curves would be 1000 W/m2 and a typical value for the MPPlow curves would be 200 W/m2. Ideally, each array of photovoltaic cells will be operating at its MPP to maximize the energy the photovoltaic system can capture. This ideal can be difficult to achieve because the I-V curve and MPP of a cell in the field is not constant.
A number of factors (“MPP factors”) influence the MPP of a given cell, module, panel, or array. They include irradiance (solar radiation energy received on a given surface area in a given time), cell temperature, spectral quality, ambient temperature, age of the panel(s), zenith and azimuth position of the sun, soiling, and wind speed. FIG. 2 is an illustrative example of MPP dependence on temperature for a fixed irradiance. FIG. 3 is an illustrative example of I-V and power curves for uniform and non-uniform irradiance. The examples are given for explanation and do not depict actual test results of a particular panel.
Referring to FIG. 3, I-V curves 311 and 313 correspond to uniform and non-uniform irradiance, respectively. Power curves 321 and 323 correspond to uniform and non-uniform irradiance, respectively. MPP voltage for uniform irradiation is indicated at 331. Under circumstances of non-uniform irradiance, it is possible to have a MPP voltage at a reduced voltage and it is possible to have local MPP≠global MPP, indicated at 333.
In large scale PV systems, on the order of 100's of kilowatts to 10's or 100's of megawatts, a large number of panels or arrays of panels are used covering large ground surface areas. In these large systems, temperature-dependent losses in system components, such as wiring and transformers, also affect the MPP of the system.
Most of these factors are affected by local weather patterns, which are unpredictable and can change rapidly.
FIG. 4 is a diagram of a large solar installation with varying MPP factors for different arrays and array groups. A complication when planning large installations is that a large installation may cover variable terrain that includes hillsides, gullies, bodies of water, stands of trees, utility easements, or man-made structures. Each of these factors can affect the external MPP factors acting on nearby panels and make them behave differently from the reference. With reference to FIG. 4, array Group A's location is “ideal”—a regular grid on flat, featureless land. Array Group B may get some shade from the hill for part of the day. Array Group C is on the hill. Array Group D may be affected by the trees (transient partial shade) or the stream and lake (reflected irradiance).
Localized differences in wind speed due to different ground levels or obstructions will affect ambient and cell temperature. Thus, landscape features can cause different panels or arrays to experience differing MPP factors at any given time.
Even if the terrain is perfectly featureless, as in some plains regions, broken or moving cloud patterns can affect the MPP of the PV panels below. The more area the installation covers, the more opportunities for shifting cloud patterns or fog patches to decrease the representative accuracy of a reference. Therefore, a need exists for a scheme to operate as close as possible to the MPP tailored to the needs of large installations.
Because PV systems of the past have been relatively small, 100's of watts to 100's of kilowatts, it has been customary to attempt to keep each module, panel, or sub-array within the system independently operating at its MPP. This function, and the systems and methods that perform it, are collectively known as “Maximum Power Point Tracking” (MPPT). The MPPT function typically resides in the inverters that receive DC power produced by the PV panels and convert it to AC power. MPPT methods may be classified as predictive (based on forecasts of likely MPP) or reactive (based on real-time feedback of actual system performance). In either case, each inverter is responsible for handling the MPPT function for the PV array it is serving.
Predictive MPPT approaches set the operating point of the PV array based on a predetermined constant value (selected to represent the average MPP) or based on an algorithm that adjusts the operating point based on inputs such as time of day, actual or predicted irradiance levels, or actual or predicted cell temperature. The disadvantage of predictive MPPT is that weather-related predictions may be wrong, and the power output will be sub-optimal if unexpected weather occurs.
Reactive MPPT methods use real-time measurements of changes in power, MPP factors, or both as feedback for closed-loop control of array operating points. These allow arrays to adapt to unexpected conditions. Reactive MPPT methods include algorithms where the operating point of the array is periodically varied until the MPP is determined. The disadvantage of reactive MPPT is that the array's power output is suboptimal for considerable periods of time while the operating point is being adjusted. The disadvantage can be compounded when rapid irradiance changes, as from fast-moving broken clouds, prolong hunt time; the MPP is a moving target while the I-V curve is changing with irradiance. The disadvantage can also be aggravated for partially-shaded arrays with “lumpy” I-V curves having multiple local maxima, an example of which is depicted in FIG. 3; the system may settle on a local MPP that is not the global MPP. Finally under quickly changing irradiance conditions, MPPTs often force the array to operate on the unstable portion of the I-V curve, which is the region beyond the peak operating point where power can drop off very quickly and the closed loop tracking system can become unstable.
“Reference” reactive MPPT methods track the MPP of a representative sample, rather than on each module, panel, array, or other independently controllable unit. The operating points of the other modules, panels, or arrays are then set to the sample's MPP. The disadvantage is that the representative sample is never completely representative due to the sample's size and differences in the MPP factors between the sample and the actual PV array. Reference MPPT schemes tend to mitigate the fluctuation problems; the larger the array, the less the reference cell's MPPT operations affect total output power. In applying this technique, the larger the number of panels in an installation, the greater the chance for error due to variability in the cell, panel or array manufacturing process. Increased geographical coverage of an installation results in increased variation in external MPP factors that the PV panels may experience. Both of these factors may compromise the accuracy of reference cells in tracking MPP for large arrays.